In digital imaging systems, color management systems can be used to control conversion between the color representations of various devices, such as scanners, cameras, monitors, TV screens, printers, offset presses, corresponding media, and the like. One primary goal of such a color management system is to obtain a good match across color devices to ensure image reproduction quality. For example, a reproduced color image should appear in substantially the same colors when displayed on a LCD monitor, plasma TV screen, or on a printed frame of video, as in the original. Color management systems utilize color matching techniques to match the same appearance on an output device in the needed color intensities to accurately reproduce the original image.
Various color matching techniques can be used to adjust the numerical values sent to, or received from, differing devices such that the perceived colors reproduced on a color output device remain substantially consistent. One problem is how to deal with a color that cannot be reproduced on a particular output device when the reproducible color ranges between colors are different. There is no common method for this process. Performance often depends on the capability of each color matching method.
In order to describe the behavior of the various output devices, they must be compared (calibrated) in relation to a standard color space. Often a step called linearization is performed in order to get the most out of limited 8-bit color paths. As an intermediate result, the device gamut is described in the form of measurements which are often not immediately usable. The transformation of the measured data into a more regular form, usable by the color reproduction software/hardware application, is called profiling. After profiling, an idealized color description of the device is created. This description is called a color profile or device profile. See: “The GATF Practical Guide to Color Management”, by: Adams & Weisberg, GATF Press (2000) ISBN 0883622483.
In color reproduction, gamuts are commonly represented as areas with a curved edge representing the monochromatic colors. Gamut areas typically have triangular shapes (or tetrahedrons) because color reproduction is often done with three primary colors. One common usage refers to the subset of colors which can be accurately represented in a given circumstance, such as within a given color space or by a certain output device. Another usage refers to the complete set of colors found within an image at a given time. In this context, converting a digitized image to a different color space, or outputting it to a given medium using a certain output device, generally alters the gamut because some of the original colors are lost in the process. See: “Introduction to Color Imaging Science”, by Hsien-Che Lee, Cambridge University Press (2005) ISBN 052184388X. Since different devices often don't have the same gamut, they need some rearrangement near the borders of the color gamut. For instance, some colors may need to be shifted to an inside portion of the gamut as they otherwise cannot be represented on the output device and may end up simply being clipped. When certain colors cannot be displayed within a particular color model, those colors are said to be out of gamut.
Color management systems can utilize various methods to achieve desired color reproduction results by giving a user more control of the color gamut mapping process. For example, pure red which is contained in the RGB color model gamut is out of gamut in the CMYK model. While modern techniques allow increasingly good color approximations, the complexity of these systems often makes accurate color reproduction computationally impractical. What is therefore acceptable in terms of the accuracy of color reproduction is often a trade-off between computational complexity and the limits of human visual perception.
A technique referred to as Delaunay triangulation can be used to model points in color space in applications involving color processing. The color data points are typically three dimensional. However when it is desirable to model the gamut of a color image, the Delaunay triangulation may end up containing millions of points resulting in a large number of generated triangles or tetrahedrons within the mesh. This causes the generated color model to contain a large number of vertices. Managing such a large number of vertices in the Delaunay mesh is a problem in this art to which no adequate solutions have been presented.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods for reducing the number of vertices in the Delaunay mesh generated when building a color gamut from a large number of measured image pixels.